Method and apparatus for autonomous identification of particle contamination due to isolated process events and systematic trends

ABSTRACT

A system and method for autonomously tracing a cause of particle contamination during semiconductor manufacture is provided. A contamination analysis system analyzes tool process logs together with particle contamination data for multiple process runs to determine a relationship between systematic particle contamination levels and one or more tool parameters. This relationship is used to predict expected contamination levels associated with regular usage of the tool, and to identify which tool parameters have the largest impact on expected levels of particle contamination. The contamination analysis system also identifies process logs showing unexpected deviant particle contamination levels that exceed expected contamination levels, and traces the cause of the deviant particle contamination to particular process log parameter events.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of pending U.S. patent applicationSer. No. 13/706,712, filed on Dec. 6, 2012, entitled “METHOD ANDAPPARATUS FOR AUTONOMOUS IDENTIFICATION OF PARTICLE CONTAMINATION DUE TOISOLATED PROCESS EVENTS AND SYSTEMATIC TRENDS OD TO DISCOVER HEARINGSENSITIVITY OF USER ON SMART PHONE”, and now issued as U.S. Pat. No.______. The entirety of the aforementioned application is herebyincorporated herein by reference.

TECHNICAL FIELD

The subject invention relates generally to techniques for tracing thecause of particle contamination during semiconductor manufacturing.

BACKGROUND

Progressive technological evolution of electronics and computing devicesmotivates advances in semiconductor technology. Growing consumer demandfor smaller, higher performance, and more efficient computer devices andelectronics has led to down-scaling of semiconductor devices. To meetdevice demand while restraining costs, silicon wafers upon whichsemiconductor devices are formed have increased in size.

Fabrication plants working with large wafer sizes utilize automation toimplement and control wafer processing. Such plants can be capitalintensive, and accordingly, it is desirable to maintain highly efficientoperation of fabrication equipment to minimize downtime and maximizeyields. To facilitate these goals, measurement equipment is oftenemployed to monitor fabrication equipment during wafer processing and toacquire measurement information on both the equipment and the processedwafer. The measurement information can then be analyzed to optimizefabrication equipment.

According to an example, measurement information can include tool levelinformation, which indicates a state or condition of fabricationequipment or a portion thereof, wafer metrology information specifyingphysical and/or geometric conditions of wafers being processed,electrical text information, and the like. In addition, spectroscopicdata, e.g., spectral line intensity information, can be gathered tofacilitate identification of etch endpoints by process engineers.However, in conventional fabrication environments, various measurementdata is handled independently of one another, for different purposes.Accordingly, inter-relationships among various measurement data are notleveraged for advanced optimization of fabrication processes.

The above-described deficiencies of today's semiconductor fabricationmeasurement and optimization systems are merely intended to provide anoverview of some of the problems of conventional systems, and are notintended to be exhaustive. Other problems with conventional systems andcorresponding benefits of the various non-limiting embodiments describedherein may become further apparent upon review of the followingdescription.

SUMMARY

The following presents a simplified summary in order to provide a basicand general understanding of some aspects of exemplary, non-limitingembodiments described herein. This summary is not an extensive overviewnor is intended to identify key/critical elements or to delineate thescope of the various aspects described herein. Instead, the sole purposeof this summary is to present some concepts in a simplified form as aprelude to the more detailed description of various embodiments thatfollow.

One or more embodiments of the present disclosure relate to techniquesfor autonomously tracing the cause of particle contamination duringsemiconductor fabrication to a particular parameter event in theassociated process log of a manufacturing tool. To this end, acontamination analysis system is provided that can identify expectedcontamination levels associated with regular usage of the tool, whichtool parameters are most associated with the expected contaminationlevels, unexpected process log parameter events that are associated withdetrimental levels of particle contamination, and other suchinformation.

In one or more embodiments, the contamination analysis system acceptsprocess log data corresponding to multiple process runs of asemiconductor manufacturing process. The analysis tool also allows auser to enter contamination specifications that define critical particlecounts for one or more types of particles. Using this information, thecontamination analysis system segregates process logs corresponding toruns showing significant particle contamination from process logscorresponding to normal process runs showing acceptable levels ofparticle contamination. A problem solving engine then processes thenormal process logs (omitting the process logs corresponding to deviantruns) to identify tool parameters associated with acceptable, expectedlevels of particle contamination resulting from tool degradation andmaintenance activities. Using this information, the analysis tooldefines an expected particle contamination level as a function of thetool parameters, which provides useful information about the impact ofmaintenance on particle contamination and which can be used to separatesystematic contamination trends from detrimental isolated events in thetool process logs.

In addition, the contamination analysis system can perform sensitivityanalysis using the tool process logs corresponding to detrimentalparticle levels to determine which tool parameters display deviantbehavior. Based on this sensitivity analysis, the contamination analysissystem can rank tool parameters according to their impact on both normaland unexpected particle contamination, offering users an insight intothe effects of respective tool parameters and process events on particlecontamination.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with thefollowing description and the annexed drawings. These aspects areindicative of various ways which can be practiced, all of which areintended to be covered herein. Other advantages and novel features maybecome apparent from the following detailed description when consideredin conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram illustrating an exemplary system for collectingand analyzing information relating to semiconductor production.

FIG. 2 is a block diagram of an exemplary contamination analysis systemthat correlates tool parameters and process events with normal orunexpected levels of particle contamination.

FIG. 3 is a block diagram illustrating processing functions performed byan exemplary contamination analysis system.

FIG. 4 illustrates an exemplary interface for defining contaminationspecifications.

FIG. 5 illustrates identification of detrimental tool runs.

FIG. 6 illustrates an exemplary process flow for determining particlecontamination levels for a semiconductor fabrication system as afunction of one or more tool parameters.

FIG. 7 illustrates ranking and identification of tool parameters thatimpact particle contamination levels.

FIG. 8 illustrates an iterative process for deriving a function forsystematic particle contamination as a function of tool processparameters.

FIG. 9 illustrates derivation of particle contamination functions thatisolate process events that cause unacceptable contamination levels.

FIG. 10 illustrates ranking of tool parameters based on sensitivityanalysis performed on a deviant particle function.

FIG. 11 illustrates an exemplary display output that lists deviant toolruns together with their respective identified root cause parameterevents.

FIG. 12 is a flowchart of an example methodology for identifying toolparameters of a semiconductor fabrication system that affect systematicparticle contamination.

FIG. 13 is a flowchart of an example methodology for isolatingunexpected tool parameter behaviors that cause deviant levels ofparticle contamination.

FIG. 14 is an example computing environment.

FIG. 15 is an example networking environment.

DETAILED DESCRIPTION

The subject innovation is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the present invention. It may be evident, however, thatthe present invention may be practiced without these specific details.In other instances, well-known structures and devices are shown in blockdiagram form in order to facilitate describing the present innovation.

As used in the subject specification and drawings, the terms “object,”“module,” “interface,” “component,” “system,” “platform,” “engine,”“selector,” “manager,” “unit,” “store,” “network,” “generator” and thelike are intended to refer to a computer-related entity or an entityrelated to, or that is part of, an operational machine or apparatus witha specific functionality; such entities can be either hardware, acombination of hardware and firmware, firmware, a combination ofhardware and software, software, or software in execution. In addition,entity(ies) identified through the foregoing terms are hereingenerically referred to as “functional elements.” As an example, acomponent can be, but is not limited to being, a process running on aprocessor, a processor, an object, an executable, a thread of execution,a program, and/or a computer. By way of illustration, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. Also, these components canexecute from various computer-readable storage media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). As anexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by software, or firmware applicationexecuted by a processor, wherein the processor can be internal orexternal to the apparatus and executes at least a part of the softwareor firmware application. As another example, a component can be anapparatus that provides specific functionality through electroniccomponents without mechanical parts, the electronic components caninclude a processor therein to execute software or firmware that confersat least in part the functionality of the electronic components.Interface(s) can include input/output (I/O) components as well asassociated processor(s), application(s), or API (Application ProgramInterface) component(s). While examples presented hereinabove aredirected to a component, the exemplified features or aspects also applyto object, module, interface, system, platform, engine, selector,manager, unit, store, network, and the like.

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or”. That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. In addition, the articles “a” and “an” as usedin this application and the appended claims should generally beconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form.

Furthermore, the term “set” as employed herein excludes the empty set;e.g., the set with no elements therein. Thus, a “set” in the subjectdisclosure includes one or more elements or entities. As anillustration, a set of components includes one or more components; a setof variables includes one or more variables; etc.

Various aspects or features will be presented in terms of systems thatmay include a number of devices, components, modules, and the like. Itis to be understood and appreciated that the various systems may includeadditional devices, components, modules, etc. and/or may not include allof the devices, components, modules etc. discussed in connection withthe figures. A combination of these approaches also can be used.

FIG. 1 is block diagram illustrating an exemplary system 100 forcollecting and analyzing information relating to semiconductorproduction. As shown in FIG. 1, a semiconductor fabrication system 110can receive input wafers 102 and output processed wafers 104. In anexemplary, non-limiting embodiment, semiconductor fabrication system 110can be an etch tool that removes unmasked material from input wafers 102via an etching process (e.g., wet etch, dry etch, plasma etching, etc.)to generate processed wafers 104 having cavities and features formedthereon. Semiconductor fabrication system 110 can also be a depositiontool that deposits material onto input wafers 102 to yield processedwafers 104.

A variety of measurement devices, such as spectroscope 120, tool sensors130, and device measurement equipment 140, can monitor the processperformed by semiconductor fabrication system 110 to acquire disparateinformation relating to various aspects, conditions, or results of theprocess. As an example, spectroscope 120 can acquire spectral intensityinformation which includes a set of intensities for respectivewavelengths or spectral lines observable by spectroscope 120. Spectralintensity information can be time-series data such that spectroscope 120measures intensities for respective wavelengths at regular intervals(e.g., every second, every 2 seconds, every 100 milliseconds, etc.).Spectroscope 120 can also correlate spectral intensity information withwafer IDs associated with specific wafers processed by semiconductorfabrication system 110. Accordingly, spectroscope 120 can acquirespectral intensity information individually for each wafer processed bysemiconductor fabrication system 110.

Tool sensors 130 can monitor and measure tool operation characteristicswhile semiconductor fabrication system 110 processes input wafers 102and generate corresponding sensor information. Tool sensor information,similar to spectral intensity information measured by spectroscope 120,can be time-series data correlated on a per-wafer basis. Tool sensorinformation can include measurements from a variety of sensors. Suchmeasurements can include, but are not limited to, pressures within oneor more chambers of semiconductor fabrication system 110, gas flows forone or more distinct gases, temperatures, upper radio frequency (RF)power, elapsed time since last wet-clean, age of tool parts, and thelike.

Device measurement equipment 140 can measure physical and geometricproperties of wafers and/or features fabricated on wafers. For instance,device measurement equipment 140 can measure development inspectioncritical dimension (DI-CD), final inspection critical dimension (FI-CD),etch bias, thickness, and so forth, at predetermined locations orregions of wafers. The measured properties can be aggregated on aper-location, per-wafer basis and output as device measurementinformation. Properties of wafers are typically measured beforeprocessing or after processing. Accordingly, device measurementinformation is typically time-series data acquired at a differentinterval as compared with spectral intensity information and tool sensorinformation.

During fabrication, semiconductor wafers are susceptible to particlecontamination, or introduction of undesirable materials into the wafermaterial. Particle contamination is often a result of imperfections inthe fabrication process, such as unclean or worn tools, tool failures,excessive vibration, gas impurities, unexpected irregularities in thefabrication process, or other factors. For example, contaminants in thechemicals used in the fabrication process—e.g., photoresists, acids,solvants, bases, etc.—can lead to particle contamination of theresulting semiconductor. While low levels of particle contamination donot substantially hinder operation of the semiconductor, excessivelevels of particle contamination can negatively impact reliability orlead to premature failure of the semiconductor.

Given the large number of process variables involved in semiconductormanufacture (tool parameters, tool age, recipe parameters, environmentalconditions at each step of the recipe, etc.), tracing a root causeprocess event or condition affecting contamination levels can bedifficult. For instance, a certain expected amount of particlecontamination is a result of tool wear (which itself is a function oftool age). As such, a percentage of overall particle contamination is afunction of regular and expected tool usage. However, since toolmeasurement data is not typically correlated with contaminationmeasurements, it is difficult to assess which tool parameters have thehighest impact on these systematic levels of contamination. Thisinformation would be useful to equipment owners in connection withidentifying where maintenance efforts should be focused.

Moreover, the problem of identifying critical process parametersaffecting particle contamination levels is rendered more difficult byoccurrence of unexpected process events or conditions that can alsointroduce undesirable substances into the wafer. For example, unexpectedgas exhaust backflow in a chamber of semiconductor fabrication system110 during a particular step in the fabrication process can result inincreased levels of particle contamination for the wafer being processedat the time. Particle contamination levels for wafers that experiencesuch unexpected process events are therefore outliers that deviate fromthe expected, systematic levels of particle contamination associatedwith normal tool usage. These unexpected deviations complicate theproblem of identifying sources of normal or unexpected particlecontamination.

To address these issues, a contamination analysis system 160 is providedthat leverages tool process log data and particle contamination data toautonomously identify causes of particle contamination, forecastexpected levels of contamination due to normal operation of thesemiconductor fabrication system 110, and other functions to bedescribed in more detail below. In one or more embodiments,contamination analysis system 160 can receive, as input, tool processlogs 108 and tool run particle data 112. Tool process logs 108 comprisemeasured parameter and performance data measured during respective runsof semiconductor fabrication system 110. Tool process logs 108 caninclude measurement data from one or more of the spectroscope 120, toolsensors 130, or device measurement equipment 140. Measurements recordedin tool process logs 108 can include, but are not limited to, sensorreadings (e.g., process log data such as chamber pressures, gas flows,temperatures, power, etc.), maintenance related readings (e.g., age ofparts, time since last performed maintenance, time since last batch ofresist was loaded, etc.), spectroscopy data (e.g., measured intensity atdifferent wavelengths), and/or tool and performance statistics (e.g.,time to process wafer, chemical consumption, gas consumption, etc.). Inan exemplary scenario, a tool process log can be generated by areporting component 150 at the end of each process run of semiconductorfabrication system 110. At the end of a process run, data from one ormore of the spectroscope 120, tool sensors 130, or device measurementequipment 140 can be provided to reporting component 150, whichaggregates the collected data in a tool process log for the run. A toolprocess log can correspond to a single semiconductor wafer processedduring the run, or a batch of semiconductors fabricated during the run.Tool process logs 108 can then be stored for reporting or archivalpurposes. Tool process logs 108 can be provided to contaminationanalysis system 160 either manually by an operator or automatically byreporting component 150 or a related device.

Tool run particle data 112 represents particle contamination levelsmeasured for a wafer or a batch of wafers during a given run. Thesecontamination levels are typically provided in the form of estimatedparticle counts for each of a variety of particle sizes detected in thewafer. In some systems, the particle counts represent an average numberof particles measured during processing of semiconductor material. Toolrun particle data 112 can be gathered by any suitable inspectioninstrumentation, including but not limited to optical or scanningelectron microscopes, laser surface scanning equipment, or the like.Similar to tool process logs 108, tool run particle data 112 can beprovided directly to contamination analysis system 160 by reportingcomponent 150 or a relate device, or entered manually into contaminationanalysis system 160 by an operator.

Contamination analysis system 160 processes tool process logs 108 andtool run particle data 112 in view of contamination specifications 106defined by the user. Contamination specifications 106 define a metrologydata output to be used for analysis, in the form of specifieddetrimental particle counts for one or more types of particles.Contamination specifications 106 are used by contamination analysissystem 160 to distinguish between acceptable contamination levels anddetrimental contamination levels. Contamination analysis system 160analyzes tool process logs 108 and tool run particle data 112 in view ofcontamination specifications 106 to generate analysis results 154, to bedescribed in more detail below. In general, analysis results 154 assistin identifying (a) expected particle contamination levels associatedwith regular usage of a fabrication tool (e.g., a deposition or etchingtool), (b) which tool parameters are most associated with the expectedlevels of particle contamination, and (c) unexpected process logparameter events associated with detrimental levels of particlecontamination.

FIG. 2 is a block diagram of an exemplary contamination analysis systemthat can correlate tool parameters and process events with normal orunexpected levels of particle contamination. Aspects of the systems,apparatuses, or processes explained in this disclosure can constitutemachine-executable components embodied within machine(s), e.g., embodiedin one or more computer-readable mediums (or media) associated with oneor more machines. Such components, when executed by one or moremachines, e.g., computer(s), computing device(s), automation device(s),virtual machine(s), etc., can cause the machine(s) to perform theoperations described.

Contamination analysis system 202 can include an interface component204, a separation component 206, a function generation component 208, aparameter identification component 210, one or more processors 212, andmemory 214. In various embodiments, one or more of the interfacecomponent 204, separation component 206, function generation component208, parameter identification component 210, one or more processors 212,and memory 214 can be electrically and/or communicatively coupled to oneanother to perform one or more of the functions of the contaminationanalysis system 202. In some embodiments, components 204, 206, 208, and210 can comprise software instructions stored on memory 214 and executedby processor(s) 212. The contamination analysis system 202 may alsointeract with other hardware and/or software components not depicted inFIG. 2. For example, processor(s) 212 may interact with one or moreexternal user interface devices, such as a keyboard, a mouse, a displaymonitor, a touchscreen, or other such interface devices.

Interface component 204 can be configured to receive input from andprovide output to a user of contamination analysis system 202. Forexample, interface component 204 can render an input display screen to auser that prompts for contamination specification definitions, andaccepts such definitions from the user via any suitable input mechanism(e.g., keyboard, touch screen, etc.). Separation component 206 can beconfigured to segregate tool process logs showing acceptablecontamination levels from those showing detrimental levels of particlecontamination, as defined by contamination specifications provided bythe user. Function generation component 208 can be configured to analyzetool process logs and tool run particle data to generate an outputdefining a level of particle contamination as a function of a set oftool parameters. Function generation component 208 can also beconfigured to generate functions that isolate the effects of eachindividual detrimental process run to determine the impact of unexpectedevents on particle contamination. Parameter identification component 210can be configured to leverage the functions generated by functiongeneration component 208 to identify tool parameters having the mostinfluence on particle contamination, as well as their relative impact oncontamination levels. Parameter identification component 210 can alsoidentify unexpected process log parameter events that cause isolatedparticle contamination conditions. The one or more processors 212 canperform one or more of the functions described herein with reference tothe systems and/or methods disclosed. Memory 214 can be acomputer-readable storage medium storing computer-executableinstructions and/or information for performing the functions describedherein with reference to the systems and/or methods disclosed.

FIG. 3 is a block diagram illustrating processing functions performed byan exemplary contamination analysis system. As described above inconnection with FIG. 1, contamination analysis system 308 receives, asinput, tool process logs 304 and tool run particle data 306 associatedwith one or more runs of semiconductor fabrication system 302. Toolprocess logs 304 and tool run particle data 306 can be providedautomatically to contamination analysis system 308 (e.g., by reportingcomponent 150 of FIG. 1) or provided to the system manually by a uservia interface component 314.

In addition to tool process logs 304 and tool run particle data 306,contamination analysis system 308 also receives contaminationspecifications 312 from a user via interface component 314.Contamination specifications 312 define a level of particlecontamination considered to be detrimental, and can be defined in termsof one or more different particle types or metrologies. An exemplaryinterface for defining contamination specifications 312 is described inconnection with FIG. 4. Exemplary interface 400 can be rendered to auser via interface component 314. As depicted in FIG. 4, a firstinterface section 402 is configured to receive contamination leveldefinitions for one or more particle types. First interface section 402allows a user to select one or more of large particles, mediumparticles, small particles, or total particles as factors fordetermining whether a wafer contains detrimental levels ofcontamination. Selecting a check box next to the particle type enablesentry of an upper limit particle count in the designated data field 406.AND and OR checkboxes to the right of data fields 406 allow a user tospecify whether the respective particle counts are to be treated as ORconditions (e.g., sufficient conditions for flagging a process log asdetrimental regardless of the particle counts of other particle types)or AND conditions (e.g., one of multiple particle count conditions thatmust be met before a process log is flagged as detrimental). A secondinterface section 404 allows a user to define the level or granularityof analysis—lot, run, or step—that will be applied by the contaminationanalysis system. In the present example, the detrimental contaminationcondition is defined as a process run having 30 medium particles or 30large particles. These contamination specifications are referenced bythe contamination analysis system to separate detrimental tool processruns from normal tool process runs, as will be described in more detailbelow.

Returning now to FIG. 3, processing operations performed on tool processlogs 304, tool run particle data 306, and contamination specifications312 are described. After tool process logs 304, tool run particle data306, and contamination specifications 312 are provided to contaminationanalysis system 308, separation component 310 performs particle eventand trend separation on the tool process logs 304 based on the tool runparticle data 306 and contamination specifications 312, resulting in asegregation of tool process logs 304 between normal process logs 316 anddeviant process logs 318, where each process log corresponds to aparticular tool run of semiconductor fabrication system 302.Identification of deviant tool runs is illustrated in FIG. 5. Asdepicted in FIG. 5, the contamination specifications used to identifydeviant runs are those described above in connection with exemplaryinterface 400 of FIG. 4; namely, 30 medium or large particles.Accordingly, for a series of N tool runs (where N is an integer), toolrun #3 is flagged as a deviant run, since 54 medium particles weremeasured for that run, exceeding the specification limit of 30 mediumparticles. Other tool runs having medium or large particle countsexceeding 30 will similarly be flagged by separation component 310.

Returning now to FIG. 3, based on identification of deviant tool runs asdescribed above, separation component 310 segregates tool process logs304 into two groups—normal process logs 316 associated with tool runshaving normal levels of particle contamination (tool runs A₀-A_(n)), anddeviant process logs 318 associated with tool runs having particlecontainment levels that exceed the defined contamination specifications312 (tool runs D₀-D_(m)). This segregation process separates dataassociated with normal, expected contamination trends from dataassociated with unexpected process events that caused detrimentalcontamination levels outside the systematic trend.

The segregated process logs are provided to a learning system 320comprising function generation component 322 and parameteridentification component 324. Learning system 320 can process thesegregated process logs in a number of ways to generate usefulinformation regarding the effects of various tool parameters on normalcontamination levels, identification of tool parameter events that causeunexpected levels of particle contamination, and other such information.For example, function generation component 322 can analyze normalprocess logs 316 to generate a particle contamination function 330 thatdescribes normal, expected contamination levels as a function of varioustool parameters of semiconductor fabrication system 302 (e.g., as aresult of tool wear-and-tear and maintenance activities). Parameteridentification component 324 can leverage particle contaminationfunction 330 to generate tool parameter rankings 328 that rank toolparameters according to their respective impact on normal contaminationlevels, providing users with a tool for developing maintenancestrategies for minimizing particle contamination. Learning system 320can also perform sensitivity analysis on normal process logs 316 anddeviant process logs 318 to identify unexpected tool parameter events326 that result in detrimental levels of particle contamination notpredicted by particle contamination function 330. These processingfunctions are described in more detail below.

FIG. 6 illustrates an exemplary process flow for determining particlecontamination levels for a semiconductor fabrication system as afunction of one or more tool parameters. As described above inconnection with FIG. 3, separation component 604 (similar to separationcomponents 206 or 310 of FIGS. 2 and 3, respectively) receives toolprocess logs 602 for a set of tool runs of a semiconductor fabricationsystem. Tool process logs 602 include values of X tool parameters(P₀-P_(x)) measured during the respective tool runs (e.g., by toolsensors 130 of FIG. 1). Exemplary tool parameters P₀-P_(x) include, butare not limited to, tool maintenance data (e.g., age of parts, timesince last maintenance, etc.), sensor measurements (e.g., chamberpressures, gas flows, upper RF power, RF-hours, etc.), and spectroscopydata. Each of the tool process logs 602 can correspond to a given toolrun association with a single wafer ID or a batch ID. Tool process logs602 can comprise parameter values for each tool parameter P₀-P_(x)averaged over the entire process run. Alternatively, tool process logs602 can include higher granularity tool parameter data, e.g., values fortool parameters P₀-P_(x) for each step of a recipe executed by theprocess run.

Based on contamination specifications provided by the user and tool runparticle data for the respective tool runs, separation component 604segregates tool process logs 602 into process logs for normal runs 606and process logs for deviant runs 608, as described above in connectionwith FIGS. 3-5. With the process logs for deviant runs 608 removed fromconsideration, function generation component 610 (similar to functiongeneration component 322 of FIG. 3) analyzes the process logs for normalruns 606 to determine a mathematical relationship between level ofparticle contamination P_(normal) and tool parameters P₀-P_(x). That is,based on an analysis of process logs for normal runs 606 and theassociated particle contamination levels for the corresponding runs,function generation component 610 generates the following output:

P _(normal) =f(P ₀ ,P ₁ ,P ₂ . . . P _(x)) for runs A ₀ through A_(n)  (1)

where P_(normal) is a level of particle contamination (e.g., a particlecount), and f is a function of tool parameters P₀-P_(x) based onanalysis of normal tool runs A₀ through A_(n). Function generationcomponent 610 can calculate the relationship described by equation (1)using any suitable problem solving methodology, including but notlimited to symbolic regression, simulated annealing, neural networks,least squares fit, or multi-level regression. Function generationcomponent 610 can then generate an output 612 based on the resultingfunction that characterizes the relationship between tool parametersP₀-P_(x) for normal tool runs and systematic contamination levelP_(normal) for the particular metrology defined by the contaminationspecifications.

Equation (1) provides a means to identify tool parameters that areassociated with normal, expected levels of particle contamination due tosuch factors as tool age and maintenance activities. The functionP_(normal) can provide useful information about the impact of toolmaintenance on particle contamination, as well as provide a meaningfulbaseline signal that can be used to separate systematic contaminationtrends from isolated contamination events that are sometimes encounteredduring fabrication and recorded in the tool process logs. In someapplications, output 612 can also be provided to a forecasting system614, which can leverage the calculated function for P_(normal) togenerate long-term contamination forecasts 616.

Based on the P_(normal) function of equation (1), the contaminationanalysis system described herein can identify which tool parameters havethe highest impact on normal particle contamination levels, asillustrated in FIG. 7. Function generation component 702 (similar tofunction generation component 322 or 610) generates function 704defining expected particle contamination level P_(normal) as a functionof tool parameters P₀-P_(x), as described above, based on tool processlogs for normal tool runs A₀-A_(n). Parameter identification component706 can then perform sensitivity analysis on function 704 to determine aparameter ranking 708, in which tool parameters P₀-P_(x) are rankedaccording to their respective influence on particle contaminationlevels. An exemplary output for parameter ranking 708 can list all or asubset of tool parameters P₀-P_(x) in descending order of their effecton particle contamination level P_(normal).

As noted above, embodiments of the contamination analysis systemdescribed herein can calculate function P_(normal) based on tool processlogs comprising averaged or aggregated values for each tool parameterover the course of a given process run. However, it is recognized thatsudden short-duration events can contribute to detrimental particlecontamination. Accordingly, one or more embodiments of the contaminationanalysis system described herein can also consider each step of the toolrecipe separately in order to reduce the potential for an outlier to belost because of data aggregations. Accordingly, if analysis is performedat the recipe step level, parameter ranking 708 can also specify therecipe step associated with each parameter. For example, in theexemplary parameter ranking 708 illustrated in FIG. 7, the toolparameter identified as having the most influence on contamination levelis the chamber #12 pressure measured at step 16 of the recipe. The toolparameter ranked as third-most influential on contamination levels isfocus ring age, which is represented by a usage count that increments atthe conclusion of each process run, and is therefore not associated witha particular recipe step.

Contamination level function 704 and parameter ranking 708 provideuseful insight into the causes of normal, expected levels of particlecontamination; e.g., factors such as tool age and degradation thatresult in gradual increase in particle contamination over time untilmaintenance or replacement is performed on the tool. This informationcan provide guidance as to where maintenance efforts should be focusedin order to control contamination levels while minimizing maintenancedowntime. Particle contamination function 704 can also be leveraged toforecast future levels of particle contamination (e.g., by providingfunction 704 to a forecasting system 614). By removing tool process logsshowing deviant levels of particle contamination from considerationprior to generating particle contamination function 704, one or moreembodiments of the contamination analysis system described herein canseparate systematic contamination trends from isolated, unexpectedcontamination events, resulting in a more accurate characterization ofnormal contamination level trends for a given semiconductor fabricationsystem.

In some scenarios, the contamination analysis system may perform aone-time, on-demand calculation of particle contamination functionP_(normal) and/or parameter rankings for a given set of tool processlogs provided to the system (e.g., a set of tool run data provided tothe system by a user). However, some embodiments of the contaminationanalysis system may also be configured to operate in a continuousiterative manner as new tool data is collected on a substantiallyreal-time basis. This iterative processing is illustrated in FIG. 8.Contamination analysis system 810 (similar to contamination analysissystems 202 or 308) may be configured to receive tool process logs 804and tool run particle data 806 directly from a semiconductor fabricationsystem as the new data becomes available (e.g., at the end of each toolrun), and update particle contamination function 812 in view ofcontamination specifications 808. Particle contamination function 812can be maintained in a data store 814 and used as a continuously updatedhistorical baseline for predicting future contamination levels,identifying tool parameters having the most significant impact onparticle contamination, and identifying unexpected tool parameter eventsthat cause sudden increases in particle contamination deviating fromexpected levels. Since particle contamination function 812 characterizesexpected contamination trends as a function of tool parameters P₀-P_(x),tool process logs 804 having deviant contamination levels outside theacceptable range will be separated out (e.g., by separation component310) and particle contamination function 812 will not be recalculatedwhen such deviant tool process logs are received. In addition torecalculating particle contamination function 812, contaminationanalysis system 810 can also re-estimate the associated tool parameterrankings as new (non-deviant) tool data is received by.

The techniques described above can facilitate characterization of afabrication system's normal, systematic contamination trends as afunction of tool parameters. In addition, one or more embodiments of thecontamination analysis system described herein can analyze tool processlogs of deviant runs (e.g., deviant process logs 318) to learn whichtool parameters display unexpected behavior resulting in sudden jumps incontamination levels. To this end, the contamination analysis system canassess each of the tool process logs associated with deviant processruns D₀-D_(m) in view of the normal contamination trends characterizedby P_(normal) in order to identify one or more errant tool parametersthat are the root cause of each detrimental run D₀-D_(m).

This analysis is described in more detail with reference to FIGS. 6 and9. As described above, separation component 604 receives tool processlogs 304 and tool run particle data 306 for a set of process runs of asemiconductor fabrication system, and segregates normal process logs 316for normal runs A₀-A_(n) showing normal levels of contamination fromdeviant process logs 318 for process runs D₀-D_(m) showing unexpectedlevels of particle contamination (based on metrology data defined incontamination specifications 312). The process logs for normal runsA₀-A_(n) are then used to determine the function P_(normal)characterizing the relationship between tool parameters P₀-P_(x) andexpected particle contamination levels. In order to identify unexpectedtool parameter events responsible for each deviant run D₀-D_(m),function generation component 322 generates a new particle contaminationfunction for each deviant run based on a data set comprising data fromthe process logs for normal runs A₀-A_(n) plus data from a process logof one deviant run of the set of deviant runs D₀-D_(m). The first row ofFIG. 9 illustrates this process for deviant process run D₀. Process logsfor normal runs A₀-A_(n) are combined with the process log for deviantrun D₀ to yield a new data set. Function generation component (e.g.,function generation components 208, 322, or 610 described above) thengenerates a particle contamination function P_(D0) characterizingparticle contamination (e.g., particle count) in terms of toolparameters P₀-P_(x) based on the data set comprising the process logsfor the normal runs A₀-A_(n) plus the process log for deviant run D₀.Function P_(D0) can be derived using similar techniques to those used toderive P_(normal). In similar fashion, the function generation componentgenerates new particle contamination functions for each of the m deviantprocess runs:

$\begin{matrix}{P_{D\; 0} = {{f\left( {P_{0},P_{1},{P_{2}\mspace{14mu} \ldots \mspace{14mu} P_{x}}} \right)}\mspace{14mu} {for}\mspace{14mu} {runs}\mspace{14mu} A_{0}\mspace{14mu} {through}\mspace{14mu} A_{n}\mspace{14mu} {and}\mspace{14mu} D_{0}}} & (2) \\{P_{D\; 1} = {{f\left( {P_{0},P_{1},{P_{2}\mspace{14mu} \ldots \mspace{14mu} P_{x}}} \right)}\mspace{14mu} {for}\mspace{14mu} {runs}\mspace{14mu} A_{0}\mspace{14mu} {through}\mspace{14mu} A_{n}\mspace{14mu} {and}\mspace{14mu} D_{1}}} & (3) \\{{P_{D\; 2} = {{f\left( {P_{0},P_{1},{P_{2}\mspace{14mu} \ldots \mspace{14mu} P_{x}}} \right)}\mspace{14mu} {for}\mspace{14mu} {runs}\mspace{14mu} A_{0}\mspace{14mu} {through}\mspace{14mu} A_{n}\mspace{14mu} {and}\mspace{14mu} D_{2}}}\ldots} & (4) \\{P_{D\; m} = {{f\left( {P_{0},P_{1},{P_{2}\mspace{14mu} \ldots \mspace{14mu} P_{x}}} \right)}\mspace{14mu} {for}\mspace{14mu} {runs}\mspace{14mu} A_{0}\mspace{14mu} {through}\mspace{14mu} A_{n}\mspace{14mu} {and}\mspace{14mu} D_{m}}} & (5)\end{matrix}$

This technique for treating each deviant process log individually canisolate unexpected tool parameter behavior and reduce complexity ofidentifying errant tool parameters in the process logs.

Once contamination functions P_(D0)-P_(Dm) have been derived, theparameter identification component can perform sensitivity analysis oneach function to identify which tool parameter(s) displayed deviantbehavior resulting in the unexpected level of contamination for thatrun. This deviant behavior represents an unexpected tool parameter eventthat resulted in a level of wafer contamination outside the expectedcontamination levels predicted by function P_(normal). In one exemplarytechnique illustrated in FIG. 10, the deviant tool parameter(s) can beidentified based on a comparison of the parameter ranking for normalcontamination trends (e.g., parameter ranking 708 of FIG. 7) with a newparameter ranking derived based on sensitivity analysis performed on thedeviant particle function (e.g., P_(D0)). Function generation component1002 (similar to function generation components 208, 322, 610, or 702)derives particle contamination function P_(D0) using techniquesdescribed above in connection with FIG. 9. That is, function P_(D0)characterizes the particle contamination level of a semiconductorfabrication system based on a data set comprising tool process logs fornormal runs A₀-A_(n) plus a tool process log from one deviant run (inthis case, deviant run D₀). Parameter identification component 1006(similar to parameter identification components 210, 324, or 706) thenperforms sensitivity analysis on function P_(D0) to determine aparameter ranking 1008 that ranks the tool parameters based on theirdetermined impact on particle contamination level. Parameteridentification component 1006 can determine parameter ranking 1008 usingsimilar techniques to those used to derive parameter ranking 708 fornormal contamination trends (e.g., symbolic regression, simulatedannealing, etc.).

Once parameter ranking 1008 for the normal runs A₀-A_(n) plus deviantrun D₀ has been determined, contamination analysis system can examineparameter ranking 1008 to identify a most likely cause of the unexpectedcontamination event. In one exemplary technique, the contaminationanalysis system can compare parameter ranking 1008 for the normal runsA₀-A_(n) plus deviant run D₀ with parameter ranking 708 derived basedsolely on the normal runs A₀-A_(n) to determine the change in parameterranking as a result of adding deviant run D₀. For example, if comparisonof parameter rankings 708 and 1008 shows that a tool parameter that hadbeen ranked #5 in parameter ranking 708 (that is, the fifth mostinfluential tool parameter affecting normal contamination trends) is nowranked at #1 in parameter ranking 1008 as a result of adding process rundata for deviant run D₀, contamination analysis system can generate anoutput suggesting that this tool parameter is a likely root cause of theunexpected level of contamination measured for process run D₀. In thepresent example, parameter ranking 1008 indicates that an exhaustbackflow event in chamber #12 during recipe step 16 is a most likelycause of the unexpected contamination level for process run D₀.

It is to be appreciated that the tool parameter event ranked at #1 intool ranking 1008 may not necessarily be the cause of the unexpectedcontamination level measured for D₀. That is, the comparison betweenparameter rankings 708 and 1008 may show that the tool parameter rankedat #1 in parameter ranking 708 is still ranked at #1 in parameterranking 1008. However, the comparison may also show that the toolparameter ranked at #5 in parameter ranking 708 for normal contaminationfunction P_(normal) rises to #3 in parameter ranking 1008 forcontamination function P_(D0).

Accordingly, the contamination analysis system identifies this changeand outputs this tool parameter as a likely cause of the unexpectedcontamination level measured for D₀.

Similar sensitivity analysis can be performed on the remaining functionsP_(D1)-P_(Dm) to determine unexpected tool parameter events associatedwith each deviant process run D₀-D_(m). Thus, the contamination analysissystem identifies a root cause tool parameter event for each unexpectedoccurrence of particle contamination falling outside the normalsystematic contamination trend, and outputs these root cause toolparameter events to a user (e.g., via interface component 204). Anexemplary non-limiting display output 1100 listing deviant tool runstogether with their respective identified root cause parameter events isillustrated in FIG. 11.

The contamination analysis system can also compare results for therespective deviant process runs D₀-D_(m) to determine if a singleunexpected tool parameter event is the root cause of all or a majorityof deviant process runs D₀-D_(m). For example, if the parameter rankingcomparisons described above identify the same unexpected tool parameterevent as the root cause for a significant number of the deviant processruns D₀-D_(m), the contamination analysis system can provide output to auser indicating that the identified tool parameter event is recurringintermittently, and recommending maintenance action to correct theissue.

To provide another dimension of analysis, one or more embodiments of thecontamination analysis system can also allow a user to suppress functionP_(normal) associated with normal behavior during analysis of deviantruns D₀-D_(m) in order to further isolate the detrimental event. Forexample, rather than (or in addition to) calculating function P_(D0)based on process logs for normal runs A₀-A_(n) plus the process log fora deviant run, a user may choose to view parameter rankings based onlyon the process logs for deviant runs D₀-D_(m).

To provide an additional level of granularity with regard tocontamination analysis, one or more embodiments of the contaminationanalysis system described herein can also consider particlecontamination location. That is, rather than performing the analysesdescribed above based on total particle contamination counts for eachwafer processed by a tool run, some embodiments of the contaminationanalysis system described herein can accept tool run particle data(e.g., tool run particle data 306) that includes separate particlecontamination counts for each of multiple locations on the semiconductorwafer. The contamination analysis system can leverage this moregranularized tool run particle data to determine correlations betweenprocess parameter events and particle contamination detected atparticular locations on the wafer. In one exemplary non-limitingtechnique, a semiconductor wafer can be divided into multiple sections,and tool run particle data can be measured separately for each sectionfor each process run. The contamination specifications defined by theuser (e.g., contamination specifications 312 of FIG. 3) specify thedetrimental levels of particles separately for each of the multiplesections. Using the location-specific particle data and contaminationspecifications, the contamination analysis system can generate separatecontamination functions P_(normal) and P_(D0)-P_(Dm) for each of themultiple locations. Accordingly, when a process run (e.g., process runD₇) yields a wafer having a detrimental level of particle contaminationat a particular location on the wafer (e.g., within one of the multiplesections), the contamination analysis system can provide an initialsuggestion as to the possible process-induced cause of the contaminationbased on the normal contamination function P_(normal) and the functionP_(D7) calculated for the that section of the wafer.

FIGS. 12-13 illustrate various methodologies in accordance with one ormore embodiments of the subject application. While, for purposes ofsimplicity of explanation, the one or more methodologies shown hereinare shown and described as a series of acts, it is to be understood andappreciated that the subject innovation is not limited by the order ofacts, as some acts may, in accordance therewith, occur in a differentorder and/or concurrently with other acts from that shown and describedherein. For example, those skilled in the art will understand andappreciate that a methodology could alternatively be represented as aseries of interrelated states or events, such as in a state diagram.Moreover, not all illustrated acts may be required to implement amethodology in accordance with the innovation. Furthermore, interactiondiagram(s) may represent methodologies, or methods, in accordance withthe subject disclosure when disparate entities enact disparate portionsof the methodologies. Further yet, two or more of the disclosed examplemethods can be implemented in combination with each other, to accomplishone or more features or advantages described herein.

FIG. 12 illustrates an example methodology 1200 for identifying toolparameters of a semiconductor fabrication system that affect systematicparticle contamination. Initially, at 1202, a set of tool process logsand associated tool run particle data is received for a set of tool runsof a semiconductor fabrication system. The tool process logs can includeparameter and performance data measured during respective runs ofsemiconductor fabrication system. Logged data can include, for example,pressures, temperatures, RF power, usage counts for various tools (e.g.,focus rings, mass flow controllers, etc.), time taken to process thewafer for the given process run, chemical and gas consumption, and othersuch production information. Tool process logs can also includemaintenance information, such as time since last performed maintenance,time since last batch of resist was loaded, etc.

At 1204 a counter N is set to 1. At 1206, a determination is maderegarding whether the Nth tool process log shows particle count datathat is within a set of contamination specifications. Contaminationspecifications can define one or more upper limits on acceptable levelsof particle contamination, specified in terms of particle counts. Forexample, an exemplary set of contamination specifications can definethat wafers having more than 30 medium particles or more than 30 largeparticles are to be flagged as unacceptable, while wafers having lessthan 30 medium particles and less than 30 large particles are determinedto be within the contamination specifications.

If it is determined at step 1206 that the Nth process log is withincontamination specifications, the methodology moves to step 1208, wherethe Nth tool process log is added to a set of normal process logs. Themethodology then moves to step 1210. Alternatively, if it is determinedat step 1206 that the Nth process log is not within contaminationspecifications (e.g., the process log corresponds to tool run particledata that exceeds the acceptable limits defined by the contaminationspecifications), the process moves directly to step 1210 without addingthe Nth tool process log to the set of normal process logs.

At 1210, a determination is made regarding whether all tool process logsin the set have been checked. If it is determined that there areremaining tool process logs that have not been checked, the methodologymoves to step 1212, where N is incremented, and steps 1206-1210 arerepeated for the next tool process log. Alternatively, if it isdetermined at step 1210 that all tool process logs have been checked,the methodology moves to step 1214.

At 1214, a function P_(normal) is calculated based on the set of normaltool process logs constructed at steps 1206 and 1208, and thecorresponding tool run particle data for the set of normal process logs.Function P_(normal) characterizes the relationship between toolparameters P₀-P_(x) and particle contamination level, and can be derivedusing any suitable problem solving technique, including but not limitedto symbolic regression, simulated annealing, neural networks, leastsquares fit, or multi-level regression. At 1216, tool parametersP₀-P_(x) are ranked according to their respective impact on normal,systematic particle contamination based on an analysis of contaminationfunction P_(normal). This ranked list of tool parameters can provideguidance regarding where maintenance efforts should be focused in orderto mitigate particle contamination during the semiconductor fabricationprocess.

FIG. 13 illustrates an exemplary methodology 1300 for isolatingunexpected tool parameter behaviors that cause deviant levels ofparticle contamination. Initially, at 1302, a set of normal tool processlogs generated for a semiconductor fabrication process are identified,where the normal tool process logs are those having tool run particledata that does not exceed a defined contamination specification. At1304, a function P_(normal) is calculated based on the set of normaltool process logs identified at step 1302 and corresponding tool runparticle data for the normal tool process logs, where P_(normal)characterizes the relationship between tool parameters P₀-P_(x) andparticle contamination level. At 1306, a deviant tool processing log isidentified having tool run particle data that exceeds the definedcontamination specification, the deviant tool processing logcorresponding to a deviant process run D₀.

At 1308, a function P_(D0) is calculated characterizing the relationshipbetween tool parameters P₀-P_(x) and particle contamination level. Incontrast to function P_(normal), function P_(D0) is derived based on thenormal process logs plus the deviant tool process log for deviantprocess run D₀, as well as corresponding tool run particle data for thenormal process logs and the deviant process log. At 1310, one or more ofthe tool parameters P₀-P_(x) are identified as the cause of unexpectedcontamination levels measured for deviant process run D₀ based on acomparison between functions P_(normal) and P_(D0). In one exemplarycomparison technique, tool parameters P₀-P_(x) can be ranked accordingto their relative impact on particle contamination based on analysis offunction P_(normal), and a similar ranking of tool parameters P₀-P_(x)can be derived based on function P_(D0). A comparison of the tworankings may reveal that a particular tool parameter is ranked higher inthe P_(D0) ranking than in the P_(normal) ranking, suggesting that anevent associated with the identified tool parameter caused the deviantcontamination level measured for process run D₀.

The various aspects (e.g., in connection with receiving one or moreselections, determining the meaning of the one or more selections,distinguishing a selection from other actions, implementation ofselections to satisfy the request, and so forth) can employ variousartificial intelligence-based schemes for carrying out various aspectsthereof. For example, a process for determining if a particular actionis a request for an action to be performed or a general action (e.g., anaction that the user desires to perform manually) can be enabled throughan automatic classifier system and process.

A classifier is a function that maps an input attribute vector, x=(x1,x2, x3, x4, xn), to a confidence that the input belongs to a class, thatis, f(x)=confidence (class). Such classification can employ aprobabilistic and/or statistical-based analysis (e.g., factoring intothe analysis utilities and costs) to prognose or infer an action that auser desires to be automatically performed. In the case of selections,for example, attributes can be identification of a focus chamber and/ora reference chamber and the classes are criteria of the focus chamberand/or reference chamber that need to be utilized to satisfy therequest.

A support vector machine (SVM) is an example of a classifier that can beemployed. The SVM operates by finding a hypersurface in the space ofpossible inputs, which hypersurface attempts to split the triggeringcriteria from the non-triggering events. Intuitively, this makes theclassification correct for testing data that is near, but not identicalto training data. Other directed and undirected model classificationapproaches include, for example, naïve Bayes, Bayesian networks,decision trees, neural networks, fuzzy logic models, and probabilisticclassification models providing different patterns of independence canbe employed. Classification as used herein also is inclusive ofstatistical regression that is utilized to develop models of priority.

As will be readily appreciated from the subject specification, the oneor more aspects can employ classifiers that are explicitly trained(e.g., through a generic training data) as well as implicitly trained(e.g., by observing user behavior, receiving extrinsic information). Forexample, SVM's are configured through a learning or training phasewithin a classifier constructor and feature selection module. Thus, theclassifier(s) can be used to automatically learn and perform a number offunctions, including but not limited to determining according to apredetermined criteria when to compare chambers, which chambers tocompare, what chambers to group together, relationships betweenchambers, and so forth. The criteria can include, but is not limited to,similar requests, historical information, and so forth.

Referring now to FIG. 14, illustrated is a block diagram of a computeroperable to execute the disclosed aspects. In order to provideadditional context for various aspects thereof, FIG. 14 and thefollowing discussion are intended to provide a brief, generaldescription of a suitable computing environment 1400 in which thevarious aspects of the embodiment(s) can be implemented. While thedescription above is in the general context of computer-executableinstructions that may run on one or more computers, those skilled in theart will recognize that the various embodiments can be implemented incombination with other program modules and/or as a combination ofhardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the disclosed aspects can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, micro-controllers, embeddedcontrollers, multi-core processors, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the various embodiments may also be practicedin distributed computing environments where certain tasks are performedby remote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media can include,but are not limited to, RAM, ROM, EEPROM, DRAM, flash memory or othermemory technology, CD-ROM, digital versatile disk (DVD) or other opticaldisk storage, magnetic cassettes, magnetic tape, magnetic disk storageor other magnetic storage devices, or other tangible and/ornon-transitory media which can be used to store desired information.Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules, or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, microwave, RF, infrared and other wireless methods(e.g., IEEE 802.12X, IEEE 802.15.4).

With reference again to FIG. 14, the illustrative computing environment1400 for implementing various aspects includes a computer 1402, whichincludes a processing unit 1404, a system memory 1406 and a system bus1408. The system bus 1408 couples system components including, but notlimited to, the system memory 1406 to the processing unit 1404. Theprocessing unit 1404 can be any of various commercially availableprocessors. Dual microprocessors and other multi-processor architecturesmay also be employed as the processing unit 1404.

The system bus 1408 can be any of several types of bus structure thatmay further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1406includes read-only memory (ROM) 1410 and random access memory (RAM)1412. A basic input/output system (BIOS) is stored in a non-volatilememory 1410 such as ROM, EPROM, EEPROM, which BIOS contains the basicroutines that help to transfer information between elements within thecomputer 1402, such as during start-up. The RAM 1412 can also include ahigh-speed RAM such as static RAM for caching data.

The computer 1402 further includes a disk storage 1414, which caninclude an internal hard disk drive (HDD) (e.g., EIDE, SATA), whichinternal hard disk drive may also be configured for external use in asuitable chassis (not shown), a magnetic floppy disk drive (FDD), (e.g.,to read from or write to a removable diskette) and an optical disk drive(e.g., reading a CD-ROM disk or, to read from or write to other highcapacity optical media such as the DVD). The hard disk drive, magneticdisk drive and optical disk drive can be connected to the system bus1408 by a hard disk drive interface, a magnetic disk drive interface andan optical drive interface, respectively. The interface 1416 forexternal drive implementations includes at least one or both ofUniversal Serial Bus (USB) and IEEE 1094 interface technologies. Otherexternal drive connection technologies are within contemplation of thevarious embodiments described herein.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1402, the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer, such as zipdrives, magnetic cassettes, flash memory cards, cartridges, and thelike, may also be used in the illustrative operating environment, andfurther, that any such media may contain computer-executableinstructions for performing the disclosed aspects.

A number of program modules can be stored in the drives and RAM,including an operating system 1418, one or more application programs1420, other program modules 1424, and program data 1426. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM. It is to be appreciated that the various embodimentscan be implemented with various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 1402 throughone or more wired/wireless input devices 1428, such as a keyboard and apointing device, such as a mouse. Other input devices (not shown) mayinclude a microphone, an IR remote control, a joystick, a game pad, astylus pen, touch screen, or the like. These and other input devices areoften connected to the processing unit 1404 through an input device(interface) port 1430 that is coupled to the system bus 1408, but can beconnected by other interfaces, such as a parallel port, an IEEE 1094serial port, a game port, a USB port, an IR interface, etc.

A monitor or other type of display device is also connected to thesystem bus 1408 via an output (adapter) port 1434, such as a videoadapter. In addition to the monitor, a computer typically includes otherperipheral output devices 1436, such as speakers, printers, etc.

The computer 1402 may operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1438. The remotecomputer(s) 1438 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1402, although, for purposes of brevity, only a memory/storage device1440 is illustrated.

The remote computer(s) can have a network interface 1442 that enableslogical connections to computer 1402. The logical connections includewired/wireless connectivity to a local area network (LAN) and/or largernetworks, e.g., a wide area network (WAN). Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which mayconnect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 1402 isconnected to the local network through a wired and/or wirelesscommunication network interface or adapter (communication connection(s))1444. The adaptor 1444 may facilitate wired or wireless communication tothe LAN, which may also include a wireless access point disposed thereonfor communicating with the wireless adaptor.

When used in a WAN networking environment, the computer 1402 can includea modem, or is connected to a communications server on the WAN, or hasother means for establishing communications over the WAN, such as by wayof the Internet. The modem, which can be internal or external and awired or wireless device, is connected to the system bus 1408 via theserial port interface. In a networked environment, program modulesdepicted relative to the computer 1402, or portions thereof, can bestored in the remote memory/storage device 1440. It will be appreciatedthat the network connections shown are illustrative and other means ofestablishing a communications link between the computers can be used.

The computer 1402 is operable to communicate with any wireless devicesor entities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,and so forth), and telephone. This includes at least Wi-Fi andBluetooth™ wireless technologies. Thus, the communication can be apredefined structure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet withoutwires. Wi-Fi is a wireless technology similar to that used in a cellphone that enables such devices, e.g., computers, to send and receivedata indoors and out; anywhere within the range of a base station. Wi-Finetworks use radio technologies called IEEE 802.11x (a, b, g, etc.) toprovide secure, reliable, fast wireless connectivity. A Wi-Fi networkcan be used to connect computers to each other, to the Internet, and towired networks (which use IEEE 802.3 or Ethernet).

Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands.IEEE 802.11 applies to generally to wireless LANs and provides 1 or 2Mbps transmission in the 2.4 GHz band using either frequency hoppingspread spectrum (FHSS) or direct sequence spread spectrum (DSSS). IEEE802.11a is an extension to IEEE 802.11 that applies to wireless LANs andprovides up to 54 Mbps in the 5 GHz band. IEEE 802.11a uses anorthogonal frequency division multiplexing (OFDM) encoding scheme ratherthan FHSS or DSSS. IEEE 802.11b (also referred to as 802.11 High RateDSSS or Wi-Fi) is an extension to 802.11 that applies to wireless LANsand provides 11 Mbps transmission (with a fallback to 5.5, 2 and 1 Mbps)in the 2.4 GHz band. IEEE 802.11g applies to wireless LANs and provides20+ Mbps in the 2.4 GHz band. Products can contain more than one band(e.g., dual band), so the networks can provide real-world performancesimilar to the basic 10BaseT wired Ethernet networks used in manyoffices.

Referring now to FIG. 15, there is illustrated a schematic block diagramof an illustrative computing environment 1500 for processing thedisclosed architecture in accordance with another aspect. The computingenvironment 1500 includes one or more client(s) 1502. The client(s) 1502can be hardware and/or software (e.g., threads, processes, computingdevices). The client(s) 1502 can house cookie(s) and/or associatedcontextual information in connection with the various embodiments, forexample.

The environment 1500 also includes one or more server(s) 1504. Theserver(s) 1504 can also be hardware and/or software (e.g., threads,processes, computing devices). The servers 1504 can house threads toperform transformations in connection with the various embodiments, forexample. One possible communication between a client 1502 and servers1504 can be in the form of a data packet adapted to be transmittedbetween two or more computer processes. The data packet may include acookie and/or associated contextual information, for example. Theenvironment 1500 includes a communication framework 1506 (e.g., a globalcommunication network such as the Internet) that can be employed tofacilitate communications between the client(s) 1502 and the server(s)1504.

Communications can be facilitated via a wired (including optical fiber)and/or wireless technology. The client(s) 1502 are operatively connectedto one or more client data store(s) 1508 that can be employed to storeinformation local to the client(s) 1502 (e.g., cookie(s) and/orassociated contextual information). Similarly, the server(s) 1504 areoperatively connected to one or more server data store(s) 1510 that canbe employed to store information local to the servers 1504.

In addition to the various embodiments described herein, it is to beunderstood that other similar embodiments can be used or modificationsand additions can be made to the described embodiment(s) for performingthe same or equivalent function of the corresponding embodiment(s)without deviating there from. Still further, multiple processing chipsor multiple devices can share the performance of one or more functionsdescribed herein, and similarly, storage can be effected across aplurality of devices. Accordingly, the invention should not be limitedto any single embodiment, but rather should be construed in breadth,spirit and scope in accordance with the appended claims.

The word “exemplary” is used herein to mean serving as an example,instance, or illustration. For the avoidance of doubt, the subjectmatter disclosed herein is not limited by such examples. In addition,any aspect or design described herein as “exemplary” is not necessarilyto be construed as preferred or advantageous over other aspects ordesigns, nor is it meant to preclude equivalent exemplary structures andtechniques known to those of ordinary skill in the art. Furthermore, tothe extent that the terms “includes,” “has,” “contains,” and othersimilar words are used, for the avoidance of doubt, such terms areintended to be inclusive in a manner similar to the term “comprising” asan open transition word without precluding any additional or otherelements.

The aforementioned systems have been described with respect tointeraction between several components. It can be appreciated that suchsystems and components can include those components or specifiedsub-components, some of the specified components or sub-components,and/or additional components, and according to various permutations andcombinations of the foregoing. Sub-components can also be implemented ascomponents communicatively coupled to other components rather thanincluded within parent components (hierarchical). Additionally, it canbe noted that one or more components may be combined into a singlecomponent providing aggregate functionality or divided into severalseparate sub-components, and that any one or more middle layers, such asa management layer, may be provided to communicatively couple to suchsub-components in order to provide integrated functionality. Anycomponents described herein may also interact with one or more othercomponents not specifically described herein but generally known bythose of skill in the art.

In view of the exemplary systems described above, methodologies that maybe implemented in accordance with the described subject matter can alsobe appreciated with reference to the flowcharts of the various figures.While for purposes of simplicity of explanation, the methodologies areshown and described as a series of blocks, it is to be understood andappreciated that the various embodiments are not limited by the order ofthe blocks, as some blocks may occur in different orders and/orconcurrently with other blocks from what is depicted and describedherein. Where non-sequential, or branched, flow is illustrated via aflowchart, it can be appreciated that various other branches, flowpaths, and orders of the blocks, may be implemented which achieve thesame or a similar result. Moreover, not all illustrated blocks may berequired to implement the methodologies described herein.

1. A system, comprising: a processor: and at least one non-transitorycomputer-readable medium having stored therein computer-executablecomponents, comprising: a parameter identification component configuredto: compare a normal contamination function to one or more deviantcontamination functions, wherein the normal contamination functioncharacterizes systematic particle contamination as a function of aplurality of tool parameters of a semiconductor fabrication system basedon analysis of normal tool process logs that record acceptablecontamination levels that are within a contamination specification for asemiconductor material process, and each deviant contamination functionof the one or more deviant contamination functions characterizes thesystematic particle contamination as another function of the pluralityof tool parameters based on analysis of the normal tool process logs andone distinct deviant tool process log of a plurality of deviant toolprocess logs that record deviant contamination levels that exceed thecontamination specification; and identify at least one of the pluralityof tool parameters as a cause of the systematic particle contaminationbased on comparison of the normal contamination function to the one ormore deviant contamination functions.
 2. The system of claim 1, whereinthe parameter identification component is further configured to generatea first ranking of the plurality of tool parameters for the normalcontamination function according to the respective tool parametersrelative effect on contamination level based on the normal contaminationfunction.
 3. The system of claim 2, wherein the parameter identificationcomponent is further configured to generate respective second rankingsof the plurality of tool parameters for the one or more deviantcontamination function according to the respective tool parametersrelative effect on contamination level based on the one or more deviantcontamination functions.
 4. The system of claim of claim 3, wherein theparameter identification component is further configured to identify theone of the plurality of tool parameters based on a comparison betweenthe first ranking of the plurality of tool parameters and the secondrankings of the plurality of tool parameters.
 5. The system of claim 1,wherein the plurality of tool parameters is obtained from spectroscopymeasuring spectral intensity during processing and process log dataincluding at least one of pressure data, temperature data, or powerdata, and tool maintenance data including at least one of a time since alast performed maintenance, a time since a last batch of resist wasloaded, age of one or more tool parts, an elapsed time to process awafer, chemical consumption data, or gas consumption data.
 6. The systemof claim 1, further comprising an interface component configured toreceive configuration input that defines the contamination specificationin terms of one or more upper limits on particle counts.
 7. The systemof claim 6, wherein the configuration input includes selection of one ormore metrologies associated with the one or more upper limits, the oneor more metrologies including at least one of a first count of particlesof a first type, a second count of particles of a second type, a thirdcount of particles of a third type, or a fourth count of particles ofthe first type, the second type, and the third type.
 8. A method,comprising: comparing, by a system including a processor, a normalcontamination function to one or more deviant contamination functions,wherein the normal contamination function characterizes systematicparticle contamination as a function of a plurality of tool parametersof a semiconductor fabrication system based on analysis of normal toolprocess logs that record acceptable contamination levels that are withina contamination specification for a semiconductor material process, andeach deviant contamination function of the one or more deviantcontamination functions characterizes the systematic particlecontamination as another function of the plurality of tool parametersbased on analysis of the normal tool process logs and one distinctdeviant tool process log of a plurality of deviant tool process logsthat record deviant contamination levels that exceed the contaminationspecification; and identifying, by the system, at least one of theplurality of tool parameters as a cause of the systematic particlecontamination based on comparison of the normal contamination functionto the one or more deviant contamination functions.
 9. The method ofclaim 8, further comprising generating, by the system, a first rankingof the plurality of tool parameters for the normal contaminationfunction according to the respective tool parameters relative effect oncontamination level based on the normal contamination function.
 10. Thesystem of claim 9, further comprising generating, by the system,respective second rankings of the plurality of tool parameters for theone or more deviant contamination function according to the respectivetool parameters relative effect on contamination level based on the oneor more deviant contamination functions.
 11. The method of claim ofclaim 10, further comprising identifying, by the system, the at leastone of the plurality of tool parameters based on a comparison betweenthe first ranking of the plurality of tool parameters and the secondrankings of the plurality of tool parameters.
 12. The method of claim 8,further comprising obtaining, by the system, the tool parameters fromspectroscopy measuring spectral intensity during processing and processlog data including at least one of pressure data, temperature data, orpower data, and tool maintenance data including at least one of a timesince a last performed maintenance, a time since a last batch of resistwas loaded, age of one or more tool parts, an elapsed time to process awafer, chemical consumption data, or gas consumption data.
 13. Themethod of claim 8, further comprising receiving, by the system,configuration information that defines the contamination specificationin terms of one or more upper limits on particle counts.
 14. The methodof claim 13, wherein the configuration information includes selection ofone or more metrologies associated with the one or more upper limits,the one or more metrologies including at least one of a first count ofparticles of a first type, a second count of particles of a second type,a third count of particles of a third type, or a fourth count ofparticles of the first type, the second type, and the third type.
 15. Acomputer-readable non-transitory medium having stored thereoncomputer-executable instructions that, in response to execution by asystem including a processor, cause the system to perform operations,the operations including: comparing a normal contamination function toone or more deviant contamination functions, wherein the normalcontamination function characterizes systematic particle contaminationas a function of a plurality of tool parameters of a semiconductorfabrication system based on analysis of normal tool process logs thatrecord acceptable contamination levels that are within a contaminationspecification for a semiconductor material process, and each deviantcontamination function of the one or more deviant contaminationfunctions characterizes the systematic particle contamination as anotherfunction of the plurality of tool parameters based on analysis of thenormal tool process logs and one distinct deviant tool process log of aplurality of deviant tool process logs that record deviant contaminationlevels that exceed the contamination specification; and identifying atleast one of the plurality of tool parameters as a cause of thesystematic particle contamination based on comparison of the normalcontamination function to the one or more deviant contaminationfunctions.
 16. The computer-readable non-transitory medium of claim 15,the operations further comprising generating a first ranking of theplurality of tool parameters for the normal contamination functionaccording to the respective tool parameters relative effect oncontamination level based on the normal contamination function.
 17. Thecomputer-readable non-transitory medium of claim 16, the operationsfurther comprising generating respective second rankings of theplurality of tool parameters for the one or more deviant contaminationfunction according to the respective tool parameters relative effect oncontamination level based on the one or more deviant contaminationfunctions.
 18. The computer-readable non-transitory medium of claim 17,the operations further comprising identifying the at least one of theplurality of tool parameters based on a comparison between the firstranking of the plurality of tool parameters and the second rankings ofthe plurality of tool parameters.
 19. The computer-readablenon-transitory medium of claim 15, the operations further comprisingobtaining the tool parameters from spectroscopy measuring spectralintensity during processing and process log data including at least oneof pressure data, temperature data, or power data, and tool maintenancedata including at least one of a time since a last performedmaintenance, a time since a last batch of resist was loaded, age of oneor more tool parts, an elapsed time to process a wafer, chemicalconsumption data, or gas consumption data.
 20. The computer-readablenon-transitory medium of claim 19, the operations further comprisingreceiving configuration information that defines the contaminationspecification in terms of one or more upper limits on particle counts.